ML之回归预测：利用Lasso、ElasticNet、GBDT等算法构建集成学习算法AvgModelsR对国内某平台上海6月份房价数据集【12+1】进行回归预测(模型评估、模型推理)

目录

利用Lasso、ElasticNet、GBDT等算法构建集成学习算法AvgModelsR对国内某平台上海6月份房价数据集【12+1】进行回归预测(模型评估、模型推理)

1、数据集基本信息

2、模型结果输出

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ML之回归预测：利用Lasso、ElasticNet、GBDT等算法构建集成学习算法AvgModelsR对国内某平台上海6月份房价数据集【12+1】进行回归预测(模型评估、模型推理)

ML之回归预测：利用Lasso、ElasticNet、GBDT等算法构建集成学习算法AvgModelsR对国内某平台上海6月份房价数据集【12+1】进行回归预测(模型评估、模型推理)实现

利用Lasso、ElasticNet、GBDT等算法构建集成学习算法AvgModelsR对国内某平台上海6月份房价数据集【12+1】进行回归预测(模型评估、模型推理)

1、数据集基本信息

(3000, 13) 13 3000total_price objectunit_priceobjectroomtype objectheight objectdirection objectdecorate objectareaobjectagefloat64garden objectdistrict objecttotal_price_Num float64unit_price_Num int64area_Num float64dtype: objectIndex(['total_price', 'unit_price', 'roomtype', 'height', 'direction','decorate', 'area', 'age', 'garden', 'district', 'total_price_Num','unit_price_Num', 'area_Num'],dtype='object')total_price unit_price roomtype ... total_price_Num unit_price_Num area_Num0 290万 46186元/平米2室1厅 ... 290.046186 62.791 599万 76924元/平米2室1厅 ... 599.076924 77.872 420万 51458元/平米2室1厅 ... 420.051458 81.623269.9万 34831元/平米2室2厅 ... 269.934831 77.494 383万 79051元/平米1室1厅 ... 383.079051 48.45[5 rows x 13 columns]total_price unit_price roomtype ... total_price_Num unit_price_Num area_Num2995 230万 43144元/平米1室1厅 ... 230.043144 53.312996 372万 75016元/平米1室1厅 ... 372.075016 49.592997 366万 49973元/平米2室1厅 ... 366.049973 73.242998 365万 69103元/平米2室1厅 ... 365.069103 52.822999 420万 49412元/平米2室2厅 ... 420.049412 85.00[5 rows x 13 columns]<class 'pandas.core.frame.DataFrame'>RangeIndex: 3000 entries, 0 to 2999Data columns (total 13 columns):# Column Non-Null Count Dtype --- ------ -------------- ----- 0 total_price3000 non-null object 1 unit_price 3000 non-null object 2 roomtype 3000 non-null object 3 height 3000 non-null object 4 direction 3000 non-null object 5 decorate 3000 non-null object 6 area 3000 non-null object 7 age 2888 non-null float648 garden 3000 non-null object 9 district 3000 non-null object 10 total_price_Num 3000 non-null float6411 unit_price_Num 3000 non-null int64 12 area_Num 3000 non-null float64dtypes: float64(3), int64(1), object(9)memory usage: 304.8+ KBage total_price_Num unit_price_Numarea_Numcount 2888.0000003000.0000003000.000000 3000.000000mean 2001.453601 631.953450 58939.028333 102.180667std 9.112425 631.308855 25867.208297 62.211662min 1911.000000 90.000000 11443.000000 17.05000025% 1996.000000 300.000000 40267.500000 67.28500050% .000000 437.000000 54946.000000 89.23000075% .000000 738.000000 73681.250000 119.035000max .0000009800.000000 250813.000000 801.140000

2、模型结果输出

AvgModelsR(models=(Pipeline(steps=[('robustscaler', RobustScaler()),('lasso',Lasso(alpha=0.001, random_state=1))]),Pipeline(steps=[('robustscaler', RobustScaler()),('elasticnet',ElasticNet(alpha=0.001, l1_ratio=0.9,random_state=3))]),GradientBoostingRegressor(random_state=5)))R2_res [0.9944881811696309, 0.000626615309319283, array([0.99470591, 0.99512495, 0.99435729, 0.99491104, 0.99334171])]MAE_res [-0.004994183753322101, 0.0001083601234287803, array([-0.00493338, -0.005202 , -0.00489054, -0.00498097, -0.00496404])]RMSE_res [-8.323227156546791e-05, 9.870911328329942e-06, array([-8.14778066e-05, -7.79621763e-05, -7.93078692e-05, -7.49049128e-05,-1.02508593e-04])]AvgModelsR(models=(Pipeline(steps=[('robustscaler', RobustScaler()),('lasso',Lasso(alpha=0.001, random_state=1))]),Pipeline(steps=[('robustscaler', RobustScaler()),('elasticnet',ElasticNet(alpha=0.001, l1_ratio=0.9,random_state=3))]),GradientBoostingRegressor(random_state=5)))Avg_Best_models Score value: 0.9947618159336031Avg_Best_models R2 value: 0.9947618159336031Avg_Best_models MAE value: 0.0064209273962331555Avg_Best_models MSE value: 9.023779248949011e-05Avg_Best_models模型花费时间： 0:06:14.344069

ML之回归预测：利用Lasso ElasticNet GBDT等算法构建集成学习算法AvgModelsR对国内某平台上海6月份房价数据集【12+1】进行回归预测(模型评估 模型推理)